# Spin of the Wheel: The Role and Reality of Monte Carlo Simulations

By Groenendaal, Huybert; Zagmutt, Francisco | Risk Management, August 2006 | Go to article overview

# Spin of the Wheel: The Role and Reality of Monte Carlo Simulations

Groenendaal, Huybert, Zagmutt, Francisco, Risk Management

Hurricane Katrina, September 11 and other recent disasters across the world represent tragic examples of worst-case events that have become an increasingly prominent--and permanent--feature of the modern risk management landscape. This has made the use of analytical tools such as Monte Carlo simulations more critical than ever when preparing for the kinds of doomsday situations that can ruin an enterprise overnight. But getting the most out of such methods can be anything but easy.

Monte Carlo methods are a widely used class of computational algorithms for simulating the behavior of various complex systems. They are different from other simulation methods in that they are in some way nondeterministic (usually by using random number generation). It is this inherent randomness that give a Monte Carlo simulation its name, since the random numbers it incorporates are not that dissimilar from the various games of chance found in a casino.

Because Monte Carlo methods use repetitive algorithms and a large hum her of calculations, they are best suited for computerized simulations. They are quite useful for studying inherently unpredictable systems, such as the calculation of risk in business. The extreme outcomes (e.g., 99th percentile) that can be generated by a Monte Carlo simulation, for example, can shed light on the critical risks a company is exposed to, highlighting that firm's likeliest worst-case scenarios. This is what makes Monte Carlo methods, as a whole, such a popular method of quantifying worst-case scenarios as a means of managing the risks they represent.

In a basic Monte Carlo simulation model, one assigns distributions to all the important uncertain parameters, then runs the simulation generating many thousands of scenarios. A probability weighting of parameter values is achieved by drawing from each distribution with a frequency proportional to the likelihood of the parameter's occurrence.

Monte Carlo has applications in many different industries. In banking, for example, with the new Basel II capital requirements, banks calculate how much credit they can offer their clients so that the bank has a 99.9% probability of sufficient capital by using historical data.

In areas such as business development, corporate finance and marketing, an increasing number of firms are also using Monte Carlo techniques to estimate values like the probability of a positive NPV or possible extreme outcomes of an investment (e.g., 1st and 99th percentiles).

While Monte Carlo simulation is a useful and potentially powerful technique to uncover possible worst-case scenarios and help your company plan for them, it is certainly not the only valuable technique available. A range of very useful but often less well-known techniques can be used either individually or together.

Using the Past to Plot the Future

Risk analysis models--Monte Carlo or otherwise--usually rely heavily on historical data to predict future scenarios. For example, credit risks for banks usually follow a fairly consistent pattern and can therefore be predicted with reasonable accuracy using a range of forecasting methods. Historical price data and leading indicators can be used to forecast commodity prices. While the reliance on historical data can be powerful and relatively "objective," one has to be careful when using it. Historical data will only tell you what risks your organization faces, and if patterns, risks or trends of the past can be replicated into the future. If the future business climate is likely to be considerably different from the past, it is important to take this into account through scenario building exercises.

Second, examine the role conservatism plays in a Monte Carlo simulation. Analysts often include some level of conservatism into their analysis (i.e., the model parameters reflect a "bad" scenario rather than an unbiased one). While this may seem logical at first glance, it is not advisable. …

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